PREDICTING BODY MASS OF ALASKAN MOOSE (ALCES ALCES GIGAS) USING BODY MEASUREMENTS AND CONDITION ASSESSMENT

Abstract

The ability to predict body mass (BM) of moose in the field using simple morphometric induced would be useful in assessing numerous aspects of moose biology and management. Previous studies have used length and girth measurements but  generally have ignored estimates of condition as potential predictors. We evaluated the efficacy of adding a subjective condition class (CC) index to mass-length regressions to improve estimates of body mass; we also evaluated the repeatability of standard morphometric measures. Total length (TL) was a significant but poor predictor of BM and exhibited non-constant variance of residuals. Chest girth (CG) was a better predictor of BM, but the best single predictor was TL*CG². The addition of CC to the regression improved the fit and reduced the standard error of the estimate. Total length of CG of 5 moose measured repeatedly over a 3-week period varied considerably, with coefficients of variation ranging from 1.9-5.5%. This variation is attributed to the difficulties associated with positioning moose for precise measurement in the field. Morphometric models assessed in this study are useful for predicting BM of moose generally but are not precise enough to predict seasonal changes in mass of mature moose.